
Unlike previous methods that try to embed priors in learning to generate radiance fields from a few images (PixelNerf [1]} and VisionNerf [2]}), we distill the views into a 3D SRF and then perform the learning in the 3D sparse voxel space. SuRFNet offers an alternative and a new way to learn novel view synthesis by learning to generate the entire 3D radiance field based on a small observation of the scene. Such a 3D setup benefits from structured 3D learning, thus creating a 3D prior that guarantees multi-view consistency, especially when rendering from out-of-distribution views (as we show in Section REF ). The input to the pipeline is the input partial SRFs from Section REF , where the goal is learning a generalizable network that converts partial SRFs to whole SRFs as can be seen in Figure REF .
